Improving the design process for factories: Modeling human performance variation

Theprocess of manufacturing system design frequently includes modeling, and usually, this means applying a technique such as discrete event simulation (DES). However, the computer tools currently available to apply this technique enable only a superficial representation of the people that operate within the systems. This is a serious limitation because the performance of people remains central to the competitiveness of many manufacturing enterprises. Therefore, this paper explores the use of probability density functions to represent the variation of worker activity times within DES models.

[1]  Subhash C. Sarin,et al.  A survey of the assembly line balancing procedures , 1998 .

[2]  Peter Ball.  Abstracting performance in hierarchical manufacturing simulation , 1998 .

[3]  Dennis E. Blumenfeld,et al.  A simple formula for estimating throughput of serial production lines with variable processing times and limited buffer capacity , 1990 .

[4]  R. Conrad,et al.  The rate of "paced" man-machine systems , 1954 .

[5]  C. Cooper,et al.  International review of industrial and organizational psychology , 1986 .

[6]  John R. Wilson,et al.  Allowing for the human element: Human factors in small manufacturing enterprises , 1993 .

[7]  B. L. Dietrich.  A Taxonomy of Discrete Manufacturing Systems , 1991, Oper. Res..

[8]  Sarah Fletcher,et al.  Towards a theoretical framework for human performance modelling within manufacturing systems design , 2005, Simul. Model. Pract. Theory.

[9]  Horst Tempelmeier,et al.  Practical considerations in the optimization of flow production systems , 2003 .

[10]  Norman A. Dudley,et al.  WORK-TIME DISTRIBUTIONS , 1963 .

[11]  Tim Baines,et al.  Humans: the missing link in manufacturing simulation? , 2004, Simul. Model. Pract. Theory.

[12]  Peter Checkland,et al.  Systems Thinking, Systems Practice , 1981 .

[13]  Bart L. MacCarthy,et al.  Human performance in planning and scheduling , 2001 .

[14]  Colin L. Moodie,et al.  OPTIMAL BUFFER STORAGE CAPACITY IN PRODUCTION , 1968 .

[15]  David Minors,et al.  Circadian Rhythms and the Human , 1981 .

[16]  Steven R Schmid Kalpakjian,et al.  Manufacturing Engineering and Technology , 1991 .

[17]  Tim Baines,et al.  Using empirical evidence of variations in worker performance to extend the capabilities of discrete event simulations in manufacturing , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[18]  Allan Carrie,et al.  Simulation of manufacturing systems , 1988 .

[19]  Averill M. Law,et al.  Simulation modelling and analysis , 1991 .

[20]  We-Min Chow,et al.  Buffer capacity analysis for sequential production lines with variable process times , 1987 .

[21]  R. J. Sury,et al.  AN INDUSTRIAL STUDY OF PACED AND UNPACED OPERATOR PERFORMANCE IN A SINGLE STAGE WORK TASK , 1964 .

[22]  Henry Herper,et al.  Modelling strain of manual work in manufacturing systems , 1994, Proceedings of Winter Simulation Conference.

[23]  Soumen Ghosh,et al.  A comprehensive literature review and analysis of the design, balancing and scheduling of assembly systems , 1989 .

[24]  Robert A. Roe,et al.  Work performance. A multiple regulation perspective , 1999 .

[25]  G. S. Fishman.  Principles of Discrete Event Simulation , 1978 .

[26]  Richard H. Weston,et al.  On the explicit modelling of systems of human resources , 2001 .

[27]  Pär Klingstam,et al.  Overview of simulation tools for computer-aided production engineering , 1999 .

[28]  A. D. Knott,et al.  THE INEFFICIENCY OF A SERIES OF WORK STATIONS—A SIMPLE FORMULA , 1970 .

[29]  Tim Baines,et al.  Human performance modelling as an aid in the process of manufacturing system design: A pilot study , 2002 .

[30]  Elwood Spencer Buffa,et al.  Modern production/ operations management , 1961 .

[31]  Shiramura Shingo,et al.  A Study of the Toyota Production System From an Industrial Engineering Viewpoint , 1989 .

[32]  Elsa Henriques,et al.  An architecture to support the manufacturing system design and planning , 2003, Int. J. Comput. Integr. Manuf..

[33]  P. C. Pandey,et al.  Efficiency of manual flow line systems—predictive equations , 1987 .

[34]  C.-L. Lin,et al.  Predictive models for performance evaluation of serial production lines , 1996 .

[35]  G. G. Hitchings,et al.  The effects of performance time variance on a balanced, four-station manual assembly line , 1973 .

[36]  K. F. H. Murrell,et al.  OPERATOR VARIABILITY AND ITS INDUSTRIAL CONSEQUENCES , 1961 .

[37]  Gunnar Bolmsjö,et al.  Simulation integration in manufacturing system development: a study of Japanese industry , 2001, Ind. Manag. Data Syst..

[38]  Frederick Winslow Taylor,et al.  科学管理原理=The principles of scientific management , 2014 .

[39]  Geoff Buxey.  The nature of manual, moving belt flowlines with overlapping stations , 1979 .

[40]  S. Gilbertova,et al.  CRITERIA ON EVALUATION OF MONOTONY OF WORK , 1970 .

[41]  Tim Baines,et al.  Humans:the missing link in simulation? , 2002 .

[42]  Ray Wild,et al.  The nonsteady-state performance of unpaced manual assembly lines , 1980 .

[43]  Henry Herper,et al.  Simulation of workers in manufacturing systems , 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

[44]  K. Knott.  A Study of Work-Time Distributions on Unpaced Tasks , 1987 .

[45]  Felix T.S. Chan,et al.  Simulation‐aided design of production and assembly cells in an automotive company , 1999 .

[46]  R. J. Sury,et al.  Aspects of assembly line balancing , 1971 .

[47]  Ezey M. Dar-El,et al.  Predicting the performance of unpaced assembly lines where one station variability may be smaller than the others , 1989 .

[48]  Herbert A. Simon,et al.  Prediction and Prescription in Systems Modeling , 1990, Oper. Res..

[49]  Z. Y. Zhao,et al.  A case for intelligent representation of dynamic resources in simulation , 1997 .